
'''simple docstring'''
import pytest

from datasets import inspect_metric, list_metrics, load_metric







@pytest.fixture
def 		A_(     A						:		Union[str, Any]):
							monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'		,     set())







@pytest.fixture
def 		A_(     A						:		Dict):


							class 			SCREAMING_SNAKE_CASE__      :







											def __init__(       self						,				A_     )->		Union[str, Any]:







																		'''simple docstring'''






																		UpperCamelCase   			=     metric_id


							class 			SCREAMING_SNAKE_CASE__      :
											lowerCAmelCase_     			=							[MetricMock(snake_case_) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]







											def 						UpperCAmelCase_    (       self     )->		int:







																		'''simple docstring'''






																		return self._metrics


							monkeypatch.setattr('datasets.inspect.huggingface_hub'		,     HfhMock())







@pytest.mark.parametrize(
    'func, args'		,     [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))])
def 		A_(     A						:		int		,     A						:		List[str]		,     A						:		Union[str, Any]		,     A						:		List[str]		,     A						:		Tuple):
							if "tmp_path" in args:
														UpperCamelCase   			=     tuple(arg if arg != 'tmp_path' else tmp_path for arg in args)
							with pytest.warns(A		,     match='https://huggingface.co/docs/evaluate'):
														func(*A)












"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)